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        "author": "linkin",
        "author_email": "yooleak@outlook.com",
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            "License :: OSI Approved :: BSD License",
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        "description": "# [bloompy](https://pypi.org/project/bloompy/)\n\nAn implementation of 4 kinds of Bloom Filter in Python3.[Chinese Edition](https://github.com/01ly/bloompy/blob/master/zh-cn.md)\n> bloompy includes the standard BloomFilter,CountingBloomFilter,ScalableBloomFilter,ScalableCountingBloomFilter.\n> It's Update from **pybloom**.\n\n## Installation\n\n> pip install bloompy\n\n##  Usage\n\nThere's 4 kinds of BloomFilter you can use by bloompy.\n* **standard bloom filter**\n\nthe standard one can only query or add elements in it. \n```python\n>>> import bloompy\n>>> bf = bloompy.BloomFilter(error_rate=0.001,element_num=10**3)\n\n# query the status of the element inside the bf immediately \n# and add it into the bf.False returned indicates the element\n# does not inside the filter.\n>>> bf.add(1) \nFalse\n>>> bf.add(1)\nTrue\n>>> 1 in bf\nTrue\n>>> bf.exists(1)\nTrue\n>>> bf.add([1,2,3])\nFalse\n>>> bf.add([1,2,3])\nTrue\n>>> [1,2,3] in bf\nTrue\n>>> bf.exists([1,2,3])\nTrue\n\n# store the bf into a file.\n>>> bf.tofile('filename.suffix')\n\n# recover a bf from a file.Auto recognize which kind of filters it is.\n>>> recovered_bf = bloompy.get_filter_fromfile('filename.suffix')\n\n# or you can use a classmethod 'fromfile' of the BloomFilter Class to get\n# a BloomFilter instance from a file.Same as other kind of filter Classes .\n>>> recovered_bf = bloompy.BloomFilter.fromfile('filename.suffix')\n\n# return the total number of the elements inside the bf.\n>>> bf.count\n2\n\n# the capacity of the current filter.\n>>> bf.capacity\n1000\n\n# the bits array of the current filter. \n>>> bf.bit_array\nbitarray('00....')\n\n# the total length of the bitarray.\n>>> bf.bit_num\n14400\n\n# the hash seeds inside the filter.\n# they are prime numbers by default.It's modifiable.\n>>> bf.seeds\n[2, 3, 5, 7, 11,...]\n\n# the amount of hash functions \n>>> bf.hash_num\n10\n\n```\n* **counting bloom filter**\n\nThe counting bloom filter is a subclass of the standard bloom filter.But it supports the **delete** operation.\nIt is set inside that 4bits represent a **bit** of the standard bf. So it costs more momery than the standard bf,\nit's 4 times the standard one.\n```python\n>>> import  bloompy\n>>> cbf  = bloompy.CountingBloomFilter(error_rate=0.001,element_num=10**3)\n\n# same as the standard bf at add operation.\n>>> cbf.add(12)\nFalse\n>>> cbf.add(12)\nTrue\n>>> 12 in cbf\nTrue\n>>> cbf.count\n1\n\n# query the status of the element inside the cbf immediately \n# if the element inside the cbf,delete it.\n>>> cbf.delete(12)\nTrue\n>>> cbf.delete(12)\nFalse\n>>> 12 in cbf\nFalse\n>>> cbf.count\n0\n\n# recover a cbf from a file.Same as the bf.\n>>> recovered_cbf = bloompy.CountingBloomFilter.fromfile('filename.suffix')\n```\nYou can do any operations of the BloomFilter on it as well. \n\n\n* **scalable bloom filter**\n\nAuto increase the capacity of the filter if the current amount of inserted elements is up to the limits.\nIt's set 2times the pre capacity inside by default.\n```python\n>>> import bloompy\n>>> sbf = bloompy.ScalableBloomFilter(error_rate=0.001,initial_capacity=10**3)\n\n# at first, the sbf is at 1000 capacity limits.\n>>> len(sbf)\n0\n>>> 12 in sbf\nFalse\n>>> sbf.add(12)\nFalse\n>>> 12 in sbf \nTrue\n>>> len(sbf)\n1\n>>> sbf.filters\n[<bloompy.BloomFilter object at 0x000000000B6F5860>]\n>>> sbf.capacity\n1000\n\n# when the amount of inserting elements surpass the limits 1000.\n# the sbf appends a new filter inside it which capacity 2times 1000.\n>>> for i in range(1000):\n        sbf.add(i)\n>>> 600 in sbf\nTrue\n>>> len(sbf)\n2\n>>> sbf.filters\n[<bloompy.BloomFilter object at 0x000000000B6F5860>, <bloompy.BloomFilter object at 0x000000000B32F748>]\n>>> sbf.capacity\n3000\n\n# recover a sbf from a file.Same as bf.\n>>> recovered_sbf = bloompy.ScalableBloomFilter.fromfile('filename.suffix')\n```\nYou can do any operations of the BloomFilter on it as well. \n\n* **scalable counting bloom filter**\n\nIt's a subclass of the ScalableBloomFilter.But it supports the **delete** operation.\nYou can do any operations of the ScalableBloomFilter on it as well. \n```python\n>>> import bloompy\n>>> scbf = bloompy.SCBloomFilter(error_rate=0.001,initial_capacity=10**3)\n\n>>> scbf.add(1)\nFalse\n>>> 1 in scbf\nTrue\n>>> scbf.delete(1)\nTrue\n>>> 1 in scbf\nFalse\n>>> len(scbf)\n1\n>>> scbf.filters\n[<bloompy.CountingBloomFilter object at 0x000000000B6F5828>]\n\n# add elements in sbf to make it at a capacity limits\n>>> for i in range(1100):\n        scbf.add(i)\n>>> len(scbf)\n2\n>>> scbf.filters\n[<bloompy.CountingBloomFilter object at 0x000000000B6F5828>, <bloompy.CountingBloomFilter object at 0x000000000B6F5898>]\n\n# recover a scbf from a file.Same as bf.\n>>> recovered_scbf = bloompy.SCBloomFilter.fromfile('filename.suffix')\n```\n## Store and recover\n\nAs shown in the standard bloom filter.You can store a filter in 2 ways:\n- classmethod 'fromfile'\n- get_filter_fromfile()\n\n> if you do clearly know that there is a BloomFilter stored in a file.\n> you can recover it with:\n> \n> ``` bloompy.BloomeFilter.fromfile('filename.suffix') ```\n> \n> or it's a CountingBloomFilter inside it:\n> \n> ```bloompy.CountingBloomFilter.fromfile('filename.suffix')```\n>\n> Same as others. \n> \n> But if you don't know what kind of filter it is stored in the file.Use:\n> \n> ```bloompy.get_filter_fromfile('filename.suffix') ```\n> \n> It will auto recognize the filter stored inside a file.Then you can do something with it.\n\n\n",
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